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相关概念视频

Introduction to R01:11

Introduction to R

250
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

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Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
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Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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相关实验视频

Updated: Jun 7, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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开始使用分级响应模型:R的介绍和教程.

Rizqy Amelia Zein1,2, Hanif Akhtar3

  • 1Department of Psychology, Ludwig-Maximilians-Universität, Munich, Germany.

International journal of psychology : Journal international de psychologie
|November 12, 2024
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概括
此摘要是机器生成的。

本教程解释了分级响应模型 (GRM),在物品响应理论 (IRT) 中的一个工具,用于评估心理尺度上的测量精度. 它指导研究人员使用R包进行GRM分析,以更好地评估项目和人.

关键词:
分级响应模型的分级响应模型项目响应理论.在这个过程中,R是R.这是一个GGMIRT.一个叫做米尔特的米尔特.心理心理心理心理心理心理心理心理心理心理心理

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科学领域:

  • 心理测量 心理测量 心理测量
  • 教育测量教育的测量
  • 心理统计 心理统计

背景情况:

  • 项目响应理论 (IRT) 提供了分析测量属性的先进方法.
  • 心理尺度通常使用多种类型 (利克特式) 的项目,需要专门的分析模型.
  • 准确测量物品和人的特征对于研究有效性至关重要.

研究的目的:

  • 引入分级响应模型 (GRM) 作为测量精度的工具.
  • 引导应用研究人员在R环境中进行一维GRM分析.
  • 为了证明GRM的应用,使用来自右翼专制主义 (RWA) 规模的现实世界数据.

主要方法:

  • 使用R包,如psych, mirt和ggmirt进行GRM分析.
  • 概述分级响应模型的理论基础.
  • 详细说明数据准备,假设测试和模型拟合的步骤.

主要成果:

  • 在RWA尺度数据上演示GRM分析.
  • 关于绘制项目参数和解释结果的指导.
  • 说明GRM如何评估多种类型物品的心理测量属性.

结论:

  • 分级响应模型 (GRM) 是在心理学研究中分析多种项目的一个有价值的工具.
  • 应用研究人员可以有效地使用R包进行GRM分析.
  • GRM分析提高了对物品和人体属性的理解,提高了测量精度.